




import fire

from utils import calculate_rouge, save_json



def 				_A  (		lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   int=None  ,							**lowerCAmelCase_	:   Dict     ):
     """simple docstring"""

     lowerCAmelCase__            =					[x.strip() for x in open(lowerCAmelCase_     ).readlines()]
     lowerCAmelCase__            =					[x.strip() for x in open(lowerCAmelCase_     ).readlines()][: len(lowerCAmelCase_     )]
     lowerCAmelCase__            =					calculate_rouge(lowerCAmelCase_  ,							lowerCAmelCase_  ,							**lowerCAmelCase_     )
     if save_path is not None:
          save_json(lowerCAmelCase_  ,							lowerCAmelCase_  ,							indent=lowerCAmelCase_     )
     return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)






'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



